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   "id": "initial_id",
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     "start_time": "2025-01-13T13:35:11.957087Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import silhouette_score"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T13:35:49.677571Z",
     "start_time": "2025-01-13T13:35:41.911894Z"
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   },
   "cell_type": "code",
   "source": [
    "# 读取四张表的数据\n",
    "\n",
    "# 读取了订单和产品id的关联，csv比较大\n",
    "prior = pd.read_csv(\"./data/instacart/order_products__prior.csv\")\n",
    "\n",
    "#产品id，与过道的对应\n",
    "products = pd.read_csv(\"./data/instacart/products.csv\")\n",
    "\n",
    "#订单id和用户id的对应，csv比较大\n",
    "orders = pd.read_csv(\"./data/instacart/orders.csv\")\n",
    "\n",
    "#超市的过道，过道放的产品的品类\n",
    "aisles = pd.read_csv(\"./data/instacart/aisles.csv\")"
   ],
   "id": "ac937aceb890eb98",
   "outputs": [],
   "execution_count": 2
  },
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   "cell_type": "code",
   "source": "prior.head()",
   "id": "c9d5825a84e9d1cf",
   "outputs": [
    {
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       "   order_id  product_id  add_to_cart_order  reordered\n",
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   "source": "products.head()",
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    {
     "data": {
      "text/plain": [
       "   product_id                                       product_name  aisle_id  \\\n",
       "0           1                         Chocolate Sandwich Cookies        61   \n",
       "1           2                                   All-Seasons Salt       104   \n",
       "2           3               Robust Golden Unsweetened Oolong Tea        94   \n",
       "3           4  Smart Ones Classic Favorites Mini Rigatoni Wit...        38   \n",
       "4           5                          Green Chile Anytime Sauce         5   \n",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
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   "cell_type": "code",
   "source": "orders.head()",
   "id": "b700624ba027c7a9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
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   "cell_type": "code",
   "source": "aisles.head()",
   "id": "8893fdacb55b3099",
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    {
     "data": {
      "text/plain": [
       "   aisle_id                       aisle\n",
       "0         1       prepared soups salads\n",
       "1         2           specialty cheeses\n",
       "2         3         energy granola bars\n",
       "3         4               instant foods\n",
       "4         5  marinades meat preparation"
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   "execution_count": 6
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  {
   "metadata": {
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     "end_time": "2025-01-13T13:42:13.289363Z",
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   "cell_type": "code",
   "source": [
    "# 合并四张表到一张表\n",
    "# 用户买了哪些订单，订单里面有哪些产品，产品对应的过道，过道对应的产品类别\n",
    "_ = pd.merge(prior, products, on=[\"product_id\", \"product_id\"])\n",
    "_mg = pd.merge(_, orders, on=[\"order_id\", \"order_id\"])\n",
    "mt = pd.merge(_mg, aisles, on=[\"aisle_id\", \"aisle_id\"])"
   ],
   "id": "d2018e933c43b543",
   "outputs": [],
   "execution_count": 8
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     "end_time": "2025-01-13T13:42:22.598271Z",
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   "cell_type": "code",
   "source": "mt.head()",
   "id": "a0eaaffbb5e30c60",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered           product_name  \\\n",
       "0         2       33120                  1          1     Organic Egg Whites   \n",
       "1         2       28985                  2          1  Michigan Organic Kale   \n",
       "2         2        9327                  3          0          Garlic Powder   \n",
       "3         2       45918                  4          1         Coconut Butter   \n",
       "4         2       30035                  5          0      Natural Sweetener   \n",
       "\n",
       "   aisle_id  department_id  user_id eval_set  order_number  order_dow  \\\n",
       "0        86             16   202279    prior             3          5   \n",
       "1        83              4   202279    prior             3          5   \n",
       "2       104             13   202279    prior             3          5   \n",
       "3        19             13   202279    prior             3          5   \n",
       "4        17             13   202279    prior             3          5   \n",
       "\n",
       "   order_hour_of_day  days_since_prior_order               aisle  \n",
       "0                  9                     8.0                eggs  \n",
       "1                  9                     8.0    fresh vegetables  \n",
       "2                  9                     8.0   spices seasonings  \n",
       "3                  9                     8.0       oils vinegars  \n",
       "4                  9                     8.0  baking ingredients  "
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       "      <td>Garlic Powder</td>\n",
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     "execution_count": 9,
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   "execution_count": 9
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    }
   },
   "cell_type": "code",
   "source": [
    "print(mt.shape)  # (32434489, 14)\n",
    "print(\"-\" * 50)\n",
    "\n",
    "# 数据缺失率，没有缺失值\n",
    "mt.isnull().sum() / mt.shape[0]"
   ],
   "id": "9c6a75efcf592934",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32434489, 14)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "order_id                  0.00000\n",
       "product_id                0.00000\n",
       "add_to_cart_order         0.00000\n",
       "reordered                 0.00000\n",
       "product_name              0.00000\n",
       "aisle_id                  0.00000\n",
       "department_id             0.00000\n",
       "user_id                   0.00000\n",
       "eval_set                  0.00000\n",
       "order_number              0.00000\n",
       "order_dow                 0.00000\n",
       "order_hour_of_day         0.00000\n",
       "days_since_prior_order    0.06407\n",
       "aisle                     0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:07:24.173431Z",
     "start_time": "2025-01-13T14:06:24.689091Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# crosstab 统计各个产品类别的数量\n",
    "# 交叉表（特殊的分组工具），如果在这一步内存不够了，重启一下jupyter，还是不行，筛选一部分用户的数据，进行聚类\n",
    "# 使用 Pandas 中的 crosstab 函数，对 DataFrame mt 中的 'user_id' 和 'aisle' 两列进行交叉制表操作，生成一个用户和商品走廊之间的交叉表。\n",
    "# 创建一个交叉表，其中行表示 'user_id' 的唯一值，列表示 'aisle' 的唯一值，表格中的值是对应用户在每个商品走廊中的计数。\n",
    "# 生成的交叉表可以帮助你分析不同用户在不同商品走廊中的购买情况，从而了解用户的购买偏好和行为模式。\n",
    "cross = pd.crosstab(mt[\"user_id\"], mt[\"aisle\"])\n",
    "\n",
    "print(cross.shape)  # (206209, 134)\n",
    "print(\"-\" * 50)\n",
    "\n",
    "cross.head()"
   ],
   "id": "bc845ef87c271473",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(206209, 134)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "aisle    air fresheners candles  asian foods  baby accessories  \\\n",
       "user_id                                                          \n",
       "1                             0            0                 0   \n",
       "2                             0            3                 0   \n",
       "3                             0            0                 0   \n",
       "4                             0            0                 0   \n",
       "5                             0            2                 0   \n",
       "\n",
       "aisle    baby bath body care  baby food formula  bakery desserts  \\\n",
       "user_id                                                            \n",
       "1                          0                  0                0   \n",
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       "\n",
       "aisle    baking ingredients  baking supplies decor  beauty  beers coolers  \\\n",
       "user_id                                                                     \n",
       "1                         0                      0       0              0   \n",
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       "3                         0                      0       0              0   \n",
       "4                         0                      0       0              0   \n",
       "5                         0                      0       0              0   \n",
       "\n",
       "aisle    ...  spreads  tea  tofu meat alternatives  tortillas flat bread  \\\n",
       "user_id  ...                                                               \n",
       "1        ...        1    0                       0                     0   \n",
       "2        ...        3    1                       1                     0   \n",
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       "4        ...        0    0                       0                     1   \n",
       "5        ...        0    0                       0                     0   \n",
       "\n",
       "aisle    trail mix snack mix  trash bags liners  vitamins supplements  \\\n",
       "user_id                                                                 \n",
       "1                          0                  0                     0   \n",
       "2                          0                  0                     0   \n",
       "3                          0                  0                     0   \n",
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       "5                          0                  0                     0   \n",
       "\n",
       "aisle    water seltzer sparkling water  white wines  yogurt  \n",
       "user_id                                                      \n",
       "1                                    0            0       1  \n",
       "2                                    2            0      42  \n",
       "3                                    2            0       0  \n",
       "4                                    1            0       0  \n",
       "5                                    0            0       3  \n",
       "\n",
       "[5 rows x 134 columns]"
      ],
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th>aisle</th>\n",
       "      <th>air fresheners candles</th>\n",
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       "      <th>baby accessories</th>\n",
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       "      <th>baby food formula</th>\n",
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       "      <th>beauty</th>\n",
       "      <th>beers coolers</th>\n",
       "      <th>...</th>\n",
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       "      <th>trail mix snack mix</th>\n",
       "      <th>trash bags liners</th>\n",
       "      <th>vitamins supplements</th>\n",
       "      <th>water seltzer sparkling water</th>\n",
       "      <th>white wines</th>\n",
       "      <th>yogurt</th>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 134 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:11:46.486121Z",
     "start_time": "2025-01-13T14:11:46.229177Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 进行主成分分析，保留90%的成分，\n",
    "# 实际特征数目减少了几倍？ 134--》27\n",
    "pca = PCA(n_components=0.9)\n",
    "\n",
    "data = pca.fit_transform(cross)  # (206209, 27)\n",
    "\n",
    "print(data.shape)"
   ],
   "id": "993dc4d2d3be98f4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(206209, 27)\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:35:45.850235Z",
     "start_time": "2025-01-13T14:35:45.845247Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 把样本数量减少,为了下面画图方便，这里只取500个样本\n",
    "x = data[:1000]\n",
    "\n",
    "print(x.shape)  # (500, 27)"
   ],
   "id": "70b8e8ae86aeb71a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 27)\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:35:47.555329Z",
     "start_time": "2025-01-13T14:35:47.542449Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# KMeans聚类\n",
    "# 选择合适的K值，silhouette_score评分越高，说明聚类效果越好\n",
    "# 这里选择了4个聚类，silhouette_score评分为0.42\n",
    "km = KMeans(n_clusters=4)\n",
    "\n",
    "# 训练模型\n",
    "km.fit(x)\n",
    "\n",
    "# 预测标签\n",
    "predict = km.predict(x)\n",
    "\n",
    "np.unique(predict)"
   ],
   "id": "49a9f856a59e5331",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3], dtype=int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:35:49.360600Z",
     "start_time": "2025-01-13T14:35:49.178361Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 显示聚类的结果\n",
    "\n",
    "# 散点图\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "# 不同颜色代表不同类别\n",
    "colored = ['orange', 'green', 'blue', 'purple']\n",
    "colr= [colored[i] for i in predict] # 根据类别给每个样本赋予颜色\n",
    "\n",
    "#去选择任意两个特征来看聚类的图，可以自行调整,第2个，第20个特征\n",
    "plt.scatter(x[:, 1], x[:, 19], c=colr)"
   ],
   "id": "898bd12e46505f35",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21cdf31aa80>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:35:52.522202Z",
     "start_time": "2025-01-13T14:35:52.501008Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算silhouette_score评分\n",
    "# 评判聚类效果，轮廓系数，聚类效果很难超过0.7，可以通过这个得分去调整上面KMeans的n_clusters\n",
    "silhouette_score(x, predict)"
   ],
   "id": "788ae4116dd2ef8a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.5132231485736094)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:41:48.736522Z",
     "start_time": "2025-01-13T14:41:48.707974Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# KMeans聚类\n",
    "# 选择合适的K值，silhouette_score评分越高，说明聚类效果越好\n",
    "km = KMeans(n_clusters=3)\n",
    "\n",
    "# 训练模型\n",
    "km.fit(x)\n",
    "\n",
    "# 预测标签\n",
    "predict = km.predict(x)\n",
    "\n",
    "silhouette_score(x, predict)"
   ],
   "id": "fcdbccba26268e84",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.6341004826382279)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T14:41:42.434263Z",
     "start_time": "2025-01-13T14:41:42.404963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# KMeans聚类\n",
    "# 选择合适的K值，silhouette_score评分越高，说明聚类效果越好\n",
    "km = KMeans(n_clusters=2)\n",
    "\n",
    "# 训练模型\n",
    "km.fit(x)\n",
    "\n",
    "# 预测标签\n",
    "predict = km.predict(x)\n",
    "\n",
    "silhouette_score(x, predict)"
   ],
   "id": "92189249d69752ea",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.6985455070311766)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
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 "nbformat_minor": 5
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